scholarly journals Social Networks: Analyzing Social Information in Deep Convolutional Neural Networks Trained for Face Identification

2018 ◽  
Vol 18 (10) ◽  
pp. 1342
Author(s):  
Connor Parde ◽  
Ying Hu ◽  
Carlos Castillo ◽  
Swami Sankaranarayanan ◽  
Alice O'Toole
Keyword(s):  
Neural Networks ◽  
Social Networks ◽  
Convolutional Neural Networks ◽  
Social Information ◽  
Face Identification ◽  
Deep Convolutional Neural Networks

scholarly journals Density of Top-Layer Codes in Deep Convolutional Neural Networks Trained for Face Identification

2019 ◽  
Vol 19 (10) ◽  
pp. 261d
Author(s):  
Connor J Parde ◽  
Y. Ivette Colon ◽  
Matthew Q Hill ◽  
Rajeev Ranjan ◽  
Carlos Castillo ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Face Identification ◽  
Deep Convolutional Neural Networks

scholarly journals Social Trait Information in Deep Convolutional Neural Networks Trained for Face Identification

2019 ◽  
Vol 43 (6) ◽  
Author(s):  
Connor J. Parde ◽  
Ying Hu ◽  
Carlos Castillo ◽  
Swami Sankaranarayanan ◽  
Alice J. O'Toole
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Face Identification ◽  
Deep Convolutional Neural Networks ◽  
Social Trait

scholarly journals Understanding unconventional preprocessors in deep convolutional neural networks for face identification

2019 ◽  
Vol 1 (11) ◽  
Author(s):  
Chollette C. Olisah ◽  
Lyndon Smith
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Network Architecture ◽  
Data Augmentation ◽  
Color Space ◽  
Back Propagation ◽  
Face Identification ◽  
Deep Convolutional Neural Networks ◽  
Softmax Classifier ◽  
Deep Network

Abstract Deep convolutional neural networks have achieved huge successes in application domains like object and face recognition. The performance gain is attributed to different facets of the network architecture such as: depth of the convolutional layers, activation function, pooling, batch normalization, forward and back propagation and many more. However, very little emphasis is made on the preprocessor’s module of the network. Therefore, in this paper, the network’s preprocessing module is varied across different preprocessing approaches while keeping constant other facets of the deep network architecture, to investigate the contribution preprocessing makes to the network. Commonly used preprocessors are the data augmentation and normalization and are termed conventional preprocessors. Others are termed the unconventional preprocessors, they are: color space converters; grey-level resolution preprocessors; full-based and plane-based image quantization, Gaussian blur, illumination normalization and insensitive feature preprocessors. To achieve fixed network parameters, CNNs with transfer learning is employed. The aim is to transfer knowledge from the high-level feature vectors of the Inception-V3 network to offline preprocessed LFW target data; and features is trained using the SoftMax classifier for face identification. The experiments show that the discriminative capability of the deep networks can be improved by preprocessing RGB data with some of the unconventional preprocessors before feeding it to the CNNs. However, for best performance, the right setup of preprocessed data with augmentation and/or normalization is required. Summarily, preprocessing data before it is fed to the deep network is found to increase the homogeneity of neighborhood pixels even at reduced bit depth which serves for better storage efficiency.

scholarly journals Facial Expression Information in Deep Convolutional Neural Networks Trained for Face Identification

2019 ◽  
Vol 19 (10) ◽  
pp. 93b
Author(s):  
Y. Ivette Colon ◽  
Matthew Q Hill ◽  
Connor J Parde ◽  
Carlos D Castillo ◽  
Rajeev Ranjan ◽  
...  
Keyword(s):  
Neural Networks ◽  
Facial Expression ◽  
Convolutional Neural Networks ◽  
Face Identification ◽  
Deep Convolutional Neural Networks

scholarly journals Hierarchical Representations of Viewpoint and Illumination in Deep Convolutional Neural Networks Trained for Face Identification

2018 ◽  
Vol 18 (10) ◽  
pp. 353
Author(s):  
Matthew Hill ◽  
Connor Parde ◽  
Jun-Cheng Chen ◽  
Carlos Castillo ◽  
Volker Blanz ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Face Identification ◽  
Deep Convolutional Neural Networks ◽  
Hierarchical Representations

CNN-based Classification of Degraded Images

2020 ◽  
Vol 2020 (10) ◽  
pp. 28-1-28-7 ◽  
Author(s):  
Kazuki Endo ◽  
Masayuki Tanaka ◽  
Masatoshi Okutomi
Keyword(s):  
Neural Networks ◽  
Image Restoration ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Deep Convolutional Neural Networks ◽  
Alternative Approach ◽  
Degraded Image ◽  
Degraded Images ◽  
Straightforward Approach

Classification of degraded images is very important in practice because images are usually degraded by compression, noise, blurring, etc. Nevertheless, most of the research in image classification only focuses on clean images without any degradation. Some papers have already proposed deep convolutional neural networks composed of an image restoration network and a classification network to classify degraded images. This paper proposes an alternative approach in which we use a degraded image and an additional degradation parameter for classification. The proposed classification network has two inputs which are the degraded image and the degradation parameter. The estimation network of degradation parameters is also incorporated if degradation parameters of degraded images are unknown. The experimental results showed that the proposed method outperforms a straightforward approach where the classification network is trained with degraded images only.

scholarly journals Deep convolutional neural networks for cardiovascular vulnerable plaque detection

2019 ◽  
Vol 277 ◽  
pp. 02024 ◽  
Author(s):  
Lincan Li ◽  
Tong Jia ◽  
Tianqi Meng ◽  
Yizhe Liu
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Vulnerable Plaque ◽  
Recall Rate ◽  
Superior Performance ◽  
Learning Approaches ◽  
Deep Convolutional Neural Networks ◽  
Vulnerable Plaques ◽  
Plaque Detection

In this paper, an accurate two-stage deep learning method is proposed to detect vulnerable plaques in ultrasonic images of cardiovascular. Firstly, a Fully Convonutional Neural Network (FCN) named U-Net is used to segment the original Intravascular Optical Coherence Tomography (IVOCT) cardiovascular images. We experiment on different threshold values to find the best threshold for removing noise and background in the original images. Secondly, a modified Faster RCNN is adopted to do precise detection. The modified Faster R-CNN utilize six-scale anchors (122,162,322,642,1282,2562) instead of the conventional one scale or three scale approaches. First, we present three problems in cardiovascular vulnerable plaque diagnosis, then we demonstrate how our method solve these problems. The proposed method in this paper apply deep convolutional neural networks to the whole diagnostic procedure. Test results show the Recall rate, Precision rate, IoU (Intersection-over-Union) rate and Total score are 0.94, 0.885, 0.913 and 0.913 respectively, higher than the 1st team of CCCV2017 Cardiovascular OCT Vulnerable Plaque Detection Challenge. AP of the designed Faster RCNN is 83.4%, higher than conventional approaches which use one-scale or three-scale anchors. These results demonstrate the superior performance of our proposed method and the power of deep learning approaches in diagnose cardiovascular vulnerable plaques.

Sign in / Sign up

Export Citation Format

Share Document